Multipronged Modeling of Subcellular Self-Organization

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Cells in our body have a certain structure: nucleus and other organelles are not placed at random, but rather positioned in certain locations to optimize transport and communication inside the cell. These organelles’ positions are not pre-programmed; the organelles find their positions through a process of mechanical self-organization. Specifically, the organelles extend dynamic ‘arms’ made of cytoskeletal proteins, and use these arms to grab, push and pull each other. The ensuing tug-of-war leads to a complex mechanical equilibrium that determines organelles’ positions. These positions can be measured in microscopic images, while underlying mechanical forces are impossible to measure. This project will solve an inverse problem by reverse-engineering the forces from the positions of organelles inside the cell. This project will also address this problem from multiple directions: considering organelles to be tiny rigid particles interacting by long-range forces, considering an abstract viscous fluid made of organelles, and finally mimicking dynamics and mechanics of all essential molecules and organelles in a computer simulation. The project will use these simulations to screen hundreds of thousands of possible forces, and experimental data to distill the correct forces in the cell. The mathematical models will be developed, tested and refined for two fundamental cellular systems: mitotic spindle (molecular machine segregating chromosomes in cell division) and multiple nuclei in large muscle cells. The models will help experimentalists to understand which molecules are responsible for proper architecture of healthy cells and for defects in aging muscles and dividing cancer cells. In the process, interdisciplinary researchers will be trained, novel courses on modeling biosystems will be developed, and K-12 students will be introduced to quantitative biology.One of the fundamental challenges of cell biology is to define principles of spatial organization of the cell. Organelle positioning is essentially a mechanical phenomenon: a set of intracellular forces is responsible for placing the organelles. These forces are generated by activities of cytoskeleton, a dynamic scaffold of elastic fibers and molecular motor proteins immersed into viscous cytosol, more specifically, the microtubule-kinesin-dynein force-generating system. The main goal is to use experimental data to reverse-engineer the intracellular forces and understand underlying molecular mechanisms. To achieve this goal, novel methods to generate multiple models and automatically screen the models against the experimental data will be developed and applied to two phenomena: self-organization of a bipolar mitotic spindle in multi-centrosomal cells, and dynamic nuclear positioning in multinucleated embryonic muscle cells. Specifically, a computer code will be developed that will generate a wide class of forces and screen the forces by detecting a small minority of the forces leading to the observed cell architectures. Multiple models (organelles as particles driven by pair-wise distance-dependent forces, energy-minimization models, continuous integro-differential and agent-based models) will be developed, compared and tested, machine learning will be used to accelerate the model screening. Collaboration with two experimental labs will result in discovery of molecular mechanisms of nuclei positioning in developing muscle cells and of dynamic architecture of mitotic spindle.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.